469 lines
29 KiB
Python
469 lines
29 KiB
Python
# -*- coding: utf-8 -*-
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from concurrent.futures import ThreadPoolExecutor
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from multiprocessing import Process
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from os import getpid
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import os
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from os.path import join
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from time import time, sleep
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from traceback import format_exc
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import cv2
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import numpy as np
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import psutil
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from loguru import logger
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from enums.ExceptionEnum import ExceptionType
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from enums.ModelTypeEnum import ModelType
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from exception.CustomerException import ServiceException
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from util import ImageUtils
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from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push_video_stream, close_all_p
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from util.ImageUtils import url2Array, add_water_pic
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from util.LogUtils import init_log
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from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
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from util.QueUtil import get_no_block_queue, put_queue, clear_queue
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class PushStreamProcess(Process):
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__slots__ = ("_msg", "_push_queue", "_image_queue", '_push_ex_queue', '_hb_queue', "_context")
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def __init__(self, *args):
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super().__init__()
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# 传参
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self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
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def build_logo_url(self):
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logo = None
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if self._context["video"]["video_add_water"]:
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logo = self._msg.get("logo_url")
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if logo:
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logo = url2Array(logo, enable_ex=False)
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if logo is None:
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logo = cv2.imread(join(self._context['base_dir'], "image/logo.png"), -1)
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self._context["logo"] = logo
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@staticmethod
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def handle_image(det_xywh, det, frame_score, copy_frame, draw_config, code_list):
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code, det_result = det
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# 每个单独模型处理
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# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
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if len(det_result) > 0:
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font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
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rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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for qs in det_result:
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box, score, cls = xywh2xyxy2(qs)
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if cls not in allowedList or score < frame_score:
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continue
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label_array, color = label_arrays[cls], rainbows[cls]
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draw_painting_joint(box, copy_frame, label_array, score, color, font_config)
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if det_xywh.get(code) is None:
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det_xywh[code], code_list[code] = {}, {}
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cd = det_xywh[code].get(cls)
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if cd is None:
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code_list[code][cls] = 1
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det_xywh[code][cls] = [[cls, box, score, label_array, color]]
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else:
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code_list[code][cls] += 1
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det_xywh[code][cls].append([cls, box, score, label_array, color])
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class OnPushStreamProcess(PushStreamProcess):
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__slots__ = ()
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def run(self):
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self.build_logo_url()
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msg, context = self._msg, self._context
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base_dir, env, orFilePath, aiFilePath, logo, service_timeout, frame_score = context["base_dir"], \
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context['env'], context["orFilePath"], context["aiFilePath"], context["logo"], \
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int(context["service"]["timeout"]) + 120, context["service"]["filter"]["frame_score"]
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request_id, push_url = msg["request_id"], msg["push_url"]
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push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
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self._hb_queue
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or_video_file, ai_video_file, push_p, ex = None, None, None, None
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ex_status = True
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# 图片相似度开关
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picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
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qs_np_tmp = None
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pix_dis = 60
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try:
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init_log(base_dir, env)
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logger.info("开始实时启动推流进程！requestId:{},pid:{}, ppid:{}", request_id,os.getpid(),os.getppid())
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with ThreadPoolExecutor(max_workers=2) as t:
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# 定义三种推流、写原视频流、写ai视频流策略
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# 第一个参数时间, 第二个参数重试次数
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p_push_status, or_write_status, ai_write_status = [0, 0], [0, 0], [0, 0]
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start_time = time()
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while True:
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# 检测推流执行超时时间, 1.防止任务运行超时 2.主进程挂了,子进程运行超时
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if time() - start_time > service_timeout:
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logger.error("推流超时, requestId: {}", request_id)
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raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
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ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
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# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
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if psutil.Process(getpid()).ppid() == 1:
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logger.info("推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
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ex_status = False
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for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
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clear_queue(q)
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break
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# 获取推流的视频帧
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push_r = get_no_block_queue(push_queue)
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if push_r is not None:
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if push_r[0] == 1:
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frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
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for i, frame in enumerate(frame_list):
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pix_dis = int((frame.shape[0]//10)*1.2)
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# 复制帧用来画图
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copy_frame = frame.copy()
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det_xywh, thread_p = {}, []
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det_xywh2 = {}
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# 所有问题的矩阵集合
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qs_np = None
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qs_reurn = []
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for det in push_objs[i]:
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code, det_result = det
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# 每个单独模型处理
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# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
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if len(det_result) > 0:
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font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
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rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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for qs in det_result:
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try: # 应对NaN情况
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box, score, cls = xywh2xyxy2(qs)
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except:
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continue
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if cls not in allowedList or score < frame_score:
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continue
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label_array, color = label_arrays[cls], rainbows[cls]
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if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
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rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
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else:
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rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
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thread_p.append(rr)
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if det_xywh.get(code) is None:
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det_xywh[code] = {}
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cd = det_xywh[code].get(cls)
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if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
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if cd is None:
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det_xywh[code][cls] = [[cls, box, score, label_array, color]]
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else:
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det_xywh[code][cls].append([cls, box, score, label_array, color])
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if qs_np is None:
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qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
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box[2][0], box[2][1], box[3][0], box[3][1],
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score, cls, code],dtype=np.float32)
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else:
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result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
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box[2][0], box[2][1], box[3][0], box[3][1],
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score, cls, code],dtype=np.float32)
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qs_np = np.row_stack((qs_np, result_li))
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if logo:
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frame = add_water_pic(frame, logo, request_id)
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copy_frame = add_water_pic(copy_frame, logo, request_id)
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if len(thread_p) > 0:
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for r in thread_p:
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r.result()
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frame_merge = video_conjuncing(frame, copy_frame)
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# 写原视频到本地
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write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
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or_write_status, request_id)
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# 写识别视频到本地
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write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
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ai_video_file, ai_write_status, request_id)
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push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
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p_push_status, request_id)
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# 如果有问题, 走下面的逻辑
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if qs_np is not None:
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if len(qs_np.shape) == 1:
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qs_np = qs_np[np.newaxis,...]
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qs_np_id = qs_np.copy()
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b = np.ones(qs_np_id.shape[0])
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qs_np_id = np.column_stack((qs_np_id,b))
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if qs_np_tmp is None:
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if picture_similarity:
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qs_np_tmp = qs_np_id.copy()
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b = np.zeros(qs_np.shape[0])
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qs_reurn = np.column_stack((qs_np,b))
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else:
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qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
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if picture_similarity:
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qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
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qs_np_tmp[:, 11] += 1
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qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
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has = False
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new_lab = []
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for j in qs_reurn:
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if j[11] == 1:
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has = True
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new_lab.append(j[9])
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if has:
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for q in qs_reurn:
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if q[11] >= 1:
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cls = int(q[9])
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if not (cls in new_lab):
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continue # 为了防止其他类别被带出
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code = str(int(q[10])).zfill(3)
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if det_xywh2.get(code) is None:
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det_xywh2[code] = {}
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cd = det_xywh2[code].get(cls)
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score = q[8]
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rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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label_array, color = label_arrays[cls], rainbows[cls]
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box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
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(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
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is_new = False
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if q[11] == 1:
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is_new = True
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if cd is None:
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det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
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else:
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det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
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if len(det_xywh2) > 0:
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put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
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push_p = push_stream_result.result(timeout=60)
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ai_video_file = write_ai_video_result.result(timeout=60)
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or_video_file = write_or_video_result.result(timeout=60)
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# 接收停止指令
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if push_r[0] == 2:
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if 'stop' == push_r[1]:
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logger.info("停止推流进程, requestId: {}", request_id)
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break
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if 'stop_ex' == push_r[1]:
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ex_status = False
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logger.info("停止推流进程, requestId: {}", request_id)
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break
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del push_r
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else:
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sleep(1)
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except ServiceException as s:
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logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
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ex = s.code, s.msg
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except Exception:
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logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
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ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
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finally:
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# 关闭推流管, 原视频写对象, 分析视频写对象
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close_all_p(push_p, or_video_file, ai_video_file, request_id)
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if ex:
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code, msg = ex
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put_queue(push_ex_queue, (1, code, msg), timeout=2)
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else:
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if ex_status:
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# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
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c_time = time()
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while time() - c_time < 60:
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if image_queue.qsize() == 0 or image_queue.empty():
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break
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sleep(2)
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for q in [push_queue, image_queue, hb_queue]:
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clear_queue(q)
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logger.info("推流进程停止完成！图片队列大小: {}, requestId:{}", image_queue.qsize(), request_id)
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class OffPushStreamProcess(PushStreamProcess):
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__slots__ = ()
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def run(self):
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self.build_logo_url()
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msg, context = self._msg, self._context
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request_id = msg["request_id"]
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base_dir, env = context["base_dir"], context['env']
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push_queue, image_queue, push_ex_queue, hb_queue = self._push_queue, self._image_queue, self._push_ex_queue, \
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self._hb_queue
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aiFilePath, logo = context["aiFilePath"], context["logo"]
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ai_video_file, push_p, push_url = None, None, msg["push_url"]
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service_timeout = int(context["service"]["timeout"]) + 120
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frame_score = context["service"]["filter"]["frame_score"]
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ex = None
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ex_status = True
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# 图片相似度开关
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picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
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qs_np_tmp = None
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pix_dis = 60
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try:
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init_log(base_dir, env)
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logger.info("开始启动离线推流进程！requestId:{}", request_id)
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with ThreadPoolExecutor(max_workers=2) as t:
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# 定义三种推流、写原视频流、写ai视频流策略
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# 第一个参数时间, 第二个参数重试次数
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p_push_status, ai_write_status = [0, 0], [0, 0]
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start_time = time()
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while True:
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# 检测推流执行超时时间
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if time() - start_time > service_timeout:
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logger.error("离线推流超时, requestId: {}", request_id)
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raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
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ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
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# 系统由于各种问题可能会杀死内存使用多的进程, 自己杀掉自己
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if psutil.Process(getpid()).ppid() == 1:
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logger.info("离线推流进程检测到父进程异常停止, 自动停止推流进程, requestId: {}", request_id)
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ex_status = False
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for q in [push_queue, image_queue, push_ex_queue, hb_queue]:
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clear_queue(q)
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break
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# 获取推流的视频帧
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push_r = get_no_block_queue(push_queue)
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if push_r is not None:
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# [(1, ...] 视频帧操作
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# [(2, 操作指令)] 指令操作
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if push_r[0] == 1:
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frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
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# 处理每一帧图片
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for i, frame in enumerate(frame_list):
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pix_dis = int((frame.shape[0]//10)*1.2)
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if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
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task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
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put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
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# 复制帧用来画图
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copy_frame = frame.copy()
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# 所有问题记录字典
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det_xywh, thread_p = {}, []
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det_xywh2 = {}
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# 所有问题的矩阵集合
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qs_np = None
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qs_reurn = []
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for det in push_objs[i]:
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code, det_result = det
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# 每个单独模型处理
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# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
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if len(det_result) > 0:
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font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
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rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
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for qs in det_result:
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box, score, cls = xywh2xyxy2(qs)
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if cls not in allowedList or score < frame_score:
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continue
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label_array, color = label_arrays[cls], rainbows[cls]
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if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
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rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
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else:
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rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
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thread_p.append(rr)
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if det_xywh.get(code) is None:
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det_xywh[code] = {}
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cd = det_xywh[code].get(cls)
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if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
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if cd is None:
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det_xywh[code][cls] = [[cls, box, score, label_array, color]]
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else:
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det_xywh[code][cls].append([cls, box, score, label_array, color])
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if qs_np is None:
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qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
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box[2][0], box[2][1], box[3][0], box[3][1],
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score, cls, code],dtype=np.float32)
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else:
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result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
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box[2][0], box[2][1], box[3][0], box[3][1],
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score, cls, code],dtype=np.float32)
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qs_np = np.row_stack((qs_np, result_li))
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if logo:
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frame = add_water_pic(frame, logo, request_id)
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copy_frame = add_water_pic(copy_frame, logo, request_id)
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if len(thread_p) > 0:
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for r in thread_p:
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r.result()
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frame_merge = video_conjuncing(frame, copy_frame)
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# 写识别视频到本地
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write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
|
||
ai_video_file,
|
||
ai_write_status, request_id)
|
||
push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
|
||
p_push_status, request_id)
|
||
|
||
|
||
if qs_np is not None:
|
||
if len(qs_np.shape) == 1:
|
||
qs_np = qs_np[np.newaxis,...]
|
||
qs_np_id = qs_np.copy()
|
||
b = np.ones(qs_np_id.shape[0])
|
||
qs_np_id = np.column_stack((qs_np_id,b))
|
||
if qs_np_tmp is None:
|
||
if picture_similarity:
|
||
qs_np_tmp = qs_np_id.copy()
|
||
b = np.zeros(qs_np.shape[0])
|
||
qs_reurn = np.column_stack((qs_np,b))
|
||
else:
|
||
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
|
||
if picture_similarity:
|
||
qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
|
||
qs_np_tmp[:, 11] += 1
|
||
qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
|
||
has = False
|
||
new_lab = []
|
||
|
||
for j in qs_reurn:
|
||
if j[11] == 1:
|
||
has = True
|
||
new_lab.append(j[9])
|
||
if has:
|
||
for q in qs_reurn:
|
||
if q[11] >= 1:
|
||
cls = int(q[9])
|
||
if not (cls in new_lab):
|
||
continue # 为了防止其他类别被带出
|
||
code = str(int(q[10])).zfill(3)
|
||
if det_xywh2.get(code) is None:
|
||
det_xywh2[code] = {}
|
||
cd = det_xywh2[code].get(cls)
|
||
score = q[8]
|
||
rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
|
||
label_array, color = label_arrays[cls], rainbows[cls]
|
||
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
|
||
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
|
||
is_new = False
|
||
if q[11] == 1:
|
||
is_new = True
|
||
if cd is None:
|
||
det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
|
||
else:
|
||
det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
|
||
if len(det_xywh2) > 0:
|
||
put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
|
||
push_p = push_stream_result.result(timeout=60)
|
||
ai_video_file = write_ai_video_result.result(timeout=60)
|
||
# 接收停止指令
|
||
if push_r[0] == 2:
|
||
if 'stop' == push_r[1]:
|
||
logger.info("停止推流进程, requestId: {}", request_id)
|
||
break
|
||
if 'stop_ex' == push_r[1]:
|
||
logger.info("停止推流进程, requestId: {}", request_id)
|
||
ex_status = False
|
||
break
|
||
del push_r
|
||
else:
|
||
sleep(1)
|
||
except ServiceException as s:
|
||
logger.error("推流进程异常:{}, requestId:{}", s.msg, request_id)
|
||
ex = s.code, s.msg
|
||
except Exception:
|
||
logger.error("推流进程异常:{}, requestId:{}", format_exc(), request_id)
|
||
ex = ExceptionType.SERVICE_INNER_EXCEPTION.value[0], ExceptionType.SERVICE_INNER_EXCEPTION.value[1]
|
||
finally:
|
||
# 关闭推流管, 分析视频写对象
|
||
close_all_p(push_p, None, ai_video_file, request_id)
|
||
if ex:
|
||
code, msg = ex
|
||
put_queue(push_ex_queue, (1, code, msg), timeout=2)
|
||
else:
|
||
if ex_status:
|
||
# 关闭推流的时候, 等待1分钟图片队列处理完，如果1分钟内没有处理完, 清空图片队列, 丢弃没有上传的图片
|
||
c_time = time()
|
||
while time() - c_time < 60:
|
||
if image_queue.qsize() == 0 or image_queue.empty():
|
||
break
|
||
sleep(2)
|
||
for q in [push_queue, image_queue, hb_queue]:
|
||
clear_queue(q)
|
||
logger.info("推流进程停止完成！requestId:{}", request_id)
|